SOTAVerified

Denoising

Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

( Image credit: Beyond a Gaussian Denoiser )

Papers

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Showing 476–500 of 7282 papers

TitleStatusHype
Continuous Speculative Decoding for Autoregressive Image GenerationCode1
Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic ManipulationCode1
Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be ConsistentCode1
Diffusion-based Graph Generative MethodsCode1
1st Place Solution for the 5th LSVOS Challenge: Video Instance SegmentationCode1
RDEIC: Accelerating Diffusion-Based Extreme Image Compression with Relay Residual DiffusionCode1
Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-raysCode1
Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet ExtractionCode1
Anatomy Completor: A Multi-class Completion Framework for 3D Anatomy ReconstructionCode1
ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion ProcessCode1
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse ProblemsCode1
3D Detection and Characterisation of ALMA Sources through Deep LearningCode1
Adaptive Unfolding Total Variation Network for Low-Light Image EnhancementCode1
Content-Noise Complementary Learning for Medical Image DenoisingCode1
Diffusion Auto-regressive Transformer for Effective Self-supervised Time Series ForecastingCode1
Diffusion-based Pose Refinement and Muti-hypothesis Generation for 3D Human Pose EstimaitonCode1
Diffusion Models Beat GANs on Image ClassificationCode1
CoT-BERT: Enhancing Unsupervised Sentence Representation through Chain-of-ThoughtCode1
Diffusion Models for Constrained DomainsCode1
Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy BiasesCode1
CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack SegmentationCode1
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationCode1
COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video EditingCode1
Diffusion Action SegmentationCode1
Progressive Correspondence Pruning by Consensus LearningCode1
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All datasetsDarmstadt Noise DatasetDIV2KDNDiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81—Unverified
2Pixel-shuffling DownsamplingPSNR38.4—Unverified
3TWSCPSNR37.93—Unverified
4CBDNet(Syn)PSNR37.57—Unverified
5MCWNNMPSNR37.38—Unverified
6Han et alPSNR35.95—Unverified
7FFDNetPSNR34.4—Unverified
8TNRDPSNR33.65—Unverified
9CDnCNN-BPSNR32.43—Unverified
10NLRNPSNR30.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61—Unverified